CN113220023B - A high-precision real-time path planning method for unmanned aerial vehicles - Google Patents
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Abstract
本发明为一种高精度无人机实时路径规划方法,属于无人机精确控制领域。该方法包含以下步骤:S1:建立无人机线性状态微分方程,获取无人机自身约束条件和环境约束条件;S2:建立无人机运动学误差模型;S3:将求解问题等效转化为线性方程求解问题;S4:根据环境约束条件设定多个不同的无人机路径目标;S5:利用深度学习方法进行矩阵分解求解控制函数参数;S6:判断求解的控制参数是否满足无人机自身约束条件。本发明方法能够大幅度降低计算量,通过实时导入动态几何方法求解的多个目标,并采用深度学习方法求出高精度的控制函数,实现无人机实时高精度路径规划。
The invention relates to a high-precision real-time path planning method for unmanned aerial vehicles, and belongs to the field of precise control of unmanned aerial vehicles. The method includes the following steps: S1: establish a linear state differential equation of the UAV, and obtain the constraints of the UAV itself and the environment; S2: establish a kinematic error model of the UAV; S3: convert the solving problem into a linear equivalent Equation solving problem; S4: Set a number of different UAV path targets according to environmental constraints; S5: Use deep learning method to perform matrix decomposition to solve the control function parameters; S6: Determine whether the solved control parameters meet the constraints of the UAV itself condition. The method of the invention can greatly reduce the calculation amount, import multiple targets solved by the dynamic geometric method in real time, and use the deep learning method to obtain a high-precision control function, so as to realize the real-time high-precision path planning of the UAV.
Description
技术领域technical field
本发明涉及一种高精度无人机实时路径规划方法,属于无人机精确控制领域,尤其适用于无人机多目标高精度路径规划场景。The invention relates to a high-precision UAV real-time path planning method, belongs to the field of UAV precise control, and is especially suitable for UAV multi-target high-precision path planning scenarios.
背景技术Background technique
随着无人机控制技术日趋成熟,无人机已广泛应用于战场环境侦察、地面目标打击、电力巡线、航拍等军事和民用领域。无人机路径规划在无人机应用中显得尤为重要。常用的路径规划方法可分为几何方法、启发式搜索方法、势场法等。其中,几何方法首先对环境进行几何建模,后依据一定的最优策略,选择某种搜索算法得到可行解,但当任务空间发生变化,需对任务空间重新遍历,计算量大,故而不适合动态航迹规划;启发式搜索法包括A*算法,粒子群算法,遗传算法等经典算法,这类方法会随着搜索空间的扩大,这类算法的计算复杂度会呈爆炸式增长,故而实时性不好;势场法中典型的方法是人工势场法,其优点是计算量减小,实时性提高,但其容易陷入局部最优。还有些方法只适用于离线规划,但外界环境因素是不确定的,且无人机受限于动力学约束,如最大转弯角和检测半径等,因此在应用过程中具有很大的局限性。With the maturity of UAV control technology, UAVs have been widely used in military and civilian fields such as battlefield environment reconnaissance, ground target strike, power line patrol, and aerial photography. UAV path planning is particularly important in UAV applications. Commonly used path planning methods can be divided into geometric methods, heuristic search methods, potential field methods and so on. Among them, the geometric method first performs geometric modeling of the environment, and then selects a certain search algorithm to obtain a feasible solution according to a certain optimal strategy. However, when the task space changes, the task space needs to be re-traversed, and the amount of calculation is large, so it is not suitable for Dynamic trajectory planning; heuristic search methods include classical algorithms such as A* algorithm, particle swarm algorithm, genetic algorithm, etc. With the expansion of the search space, the computational complexity of such algorithms will explode, so real-time The typical method of potential field method is artificial potential field method, which has the advantages of reducing the amount of calculation and improving real-time performance, but it is easy to fall into local optimum. Some methods are only suitable for offline planning, but the external environmental factors are uncertain, and the UAV is limited by dynamic constraints, such as the maximum turning angle and detection radius, so it has great limitations in the application process.
虽然,通过对上述进行改进或者结合能够减少计算量、提高实时性,但因此也大幅度牺牲了控制精度,且无法完成多目标动态的路径规划问题。Although the improvement or combination of the above can reduce the amount of calculation and improve the real-time performance, it also greatly sacrifices the control accuracy, and cannot complete the multi-objective dynamic path planning problem.
发明内容SUMMARY OF THE INVENTION
本发明提供一种高精度无人机实时路径规划方法,试图通过离线的方式将无人机路径规划问题转化为线性方程求解问题,大幅度降低计算量,通过实时导入动态几何方法求解的多个目标,并采用深度学习方法求出高精度的控制函数,实现无人机高精度路径规划。The invention provides a high-precision real-time path planning method for unmanned aerial vehicles, which attempts to transform the unmanned aerial vehicle path planning problem into a linear equation solving problem in an off-line manner, greatly reduces the amount of calculation, and imports the dynamic geometry method in real time to solve multiple problems. The target, and the deep learning method is used to obtain a high-precision control function to realize the high-precision path planning of the UAV.
为达到上述目的,本发明提供如下技术方案:To achieve the above object, the present invention provides the following technical solutions:
一种高精度无人机实时路径规划方法,包括如下步骤:A high-precision UAV real-time path planning method, comprising the following steps:
S1:基于小扰动线性化原理建立无人机线性状态微分方程,获取无人机自身约束条件和环境约束条件;S1: Establish the linear state differential equation of the UAV based on the principle of small disturbance linearization, and obtain the UAV's own constraints and environmental constraints;
S2:利用插值方法建立无人机运动学误差模型;S2: Use the interpolation method to establish the kinematic error model of the UAV;
S3:将无人机运动学误差模型的求解问题等效转化为线性方程求解问题;S3: Convert the solving problem of the UAV kinematic error model into a linear equation solving problem equivalently;
S4:根据环境约束条件设定多个不同的无人机路径目标;S4: Set multiple different UAV path targets according to environmental constraints;
S5:将无人机路径目标作为边界条件带入无人机的线性方程,并设定控制函数类型,利用深度学习方法进行矩阵分解求解控制函数参数;S5: Bring the path target of the UAV as a boundary condition into the linear equation of the UAV, set the control function type, and use the deep learning method to perform matrix decomposition to solve the control function parameters;
S6:判断求解的控制参数是否满足无人机自身约束条件,如果满足,则执行控制飞行;如果不满足,则删除该解,并重复步骤S5-步骤S6,直到无解满足后,修正无人机路径目标,并重新进行步骤S5-步骤S6,如果仍然无解并达到计算时间上限,则进行路径无解预警。S6: Determine whether the solved control parameters satisfy the constraints of the UAV itself, if so, execute the control flight; if not, delete the solution, and repeat steps S5-S6 until no solution is satisfied, correct the unmanned aerial vehicle If there is still no solution and the upper limit of the calculation time is reached, an early warning of no solution of the path is performed.
进一步,步骤S1所述的无人机的线性状态微分方程为一个常系数微分方程,形如:x′(t)=dx(t)/dt=A·x(t)+u(t),t∈[t0,tf],其中,A为n×n维无人机状态矩阵,根据无人机的动力学模型来确定;u(t)为关于时间的n维控制向量;x(t)为n维无人机的状态向量,n为正整数,通常为无人机速度、位置和姿态角等。Further, the linear state differential equation of the UAV described in step S1 is a constant coefficient differential equation, such as: x′(t)=dx(t)/dt=A x(t)+u(t), t∈[t 0 , t f ], where A is the n×n-dimensional UAV state matrix, which is determined according to the dynamic model of the UAV; u(t) is the n-dimensional control vector with respect to time; x( t) is the state vector of the n-dimensional UAV, n is a positive integer, usually the UAV speed, position and attitude angle.
进一步,步骤S2所述插值方法为Hermit三次样条插值法,在任意时间区间t∈[ti,ti+1]上通过插值方法能够得到一个步骤1所述的常系数微分方程在任意维度上一个的近似解的符号表达式其中,hi=ti+1-ti,τ=(t-ti)/hi,α0(τ)=2τ3-3τ2+1,α1(τ)=τ3-τ2+τ,β0(τ)=-2τ3+3τ2,β1(τ)=τ3-τ2,τ∈[0,1]。Further, the interpolation method described in step S2 is the Hermit cubic spline interpolation method. In any time interval t∈[t i , t i+1 ], a constant coefficient differential equation described in step 1 can be obtained by the interpolation method in any dimension. symbolic expression for the approximate solution of the previous Wherein, hi =t i +1 -t i , τ=(tt i )/ hi , α 0 (τ)=2τ 3 -3τ 2 +1, α 1 (τ)=τ 3 -τ 2 +τ , β 0 (τ)=−2τ 3 +3τ 2 , β 1 (τ)=τ 3 −τ 2 , τ∈[0,1].
更进一步,所述的Hermit三次样条插值法的区间为等时长的区间以便大幅度减低计算量,具体的划分段数i可根据实际的精度需求进行调整。Furthermore, the interval of the Hermit cubic spline interpolation method is an interval of equal duration so as to greatly reduce the amount of calculation, and the specific number of divisions i can be adjusted according to the actual accuracy requirement.
进一步,步骤S2所述的无人机运动学在t∈[ti,ti+1]段的误差为进一步,对误差进行二次型建模得到无人机运动学误差模型 Further, the error of the UAV kinematics in step S2 in the segment t∈[t i , t i+1 ] is Further, quadratic modeling of the error is carried out to obtain the kinematic error model of the UAV
进一步,所述的步骤S3具体为:Further, the step S3 is specifically:
S301:针对每一个分段代入Hermit三次样条插值方程,进一步,得到分段误差二次型其中,yi=[x(ti),x′(ti),x(ti+1),x′(ti+1)]T,bi=α1′E-α1hiA,di=β′1E-β1hiA,E为n×n维的单位矩阵;S301: For each segment Substitute into the Hermit cubic spline interpolation equation, and further, obtain the quadratic form of piecewise error where y i =[x(t i ), x'(t i ), x(t i+1 ), x'(t i+1 )] T , b i =α 1 ′E-α 1 h i A, d i =β′ 1 E-β 1 h i A, E is an n×n-dimensional identity matrix;
S302:由于恒大于0,且当控制变量确定时为一个常数,因此从误差二次型中删除该项;S302: Due to is always greater than 0, and is a constant when the control variable is determined, so this item is deleted from the error quadratic;
S303:无人机运动学误差模型转化为obj=min(yTFy-2By),其中,F为(2n·i+2n)×(2n·i+2n)维由Fi的对应叠加矩阵,B为(2n·i+2n)维由Bi对应叠加的向量,y=[x(t0),x′(t0),x(t1),x′(t1),…,x(tf),x′(tf)]T为(2n·i+2n)维yi的叠加;其中,x′(ti)是x(ti)在ti时刻关于时间t的导数;S303: Convert the UAV kinematic error model to obj=min(y T Fy-2By), where F is the (2n·i+2n)×(2n·i+2n) dimension corresponding to the superposition matrix of F i , B is a vector of (2n·i+2n) dimension correspondingly superimposed by B i , y=[x(t 0 ), x′(t 0 ), x(t 1 ), x′(t 1 ), . . . , x (t f ), x′(t f )] T is the superposition of (2n·i+2n) dimension y i ; where x′(t i ) is the derivative of x(t i ) at time t i with respect to time t ;
S304:满秩的情况下,obj=min(yTFy-2By)存在唯一解,且解为Fy=B。S304: In the case of full rank, there is a unique solution to obj=min(y T Fy-2By), and the solution is Fy=B.
进一步,步骤S4具体为:根据环境约束条件,通过几何方法进行路径规划,选取飞行途经中点或状态作为路径目标,并将其数值设定为路径y=[x(t0),x′(t0),x(t1),x′(t1),…,x(tf),x′(tf)]T中的多个不同的无人机路径目标数值,即y中部分参数已知。Further, step S4 is specifically as follows: according to the environmental constraints, the path planning is carried out by the geometric method, the midpoint or state of the flight path is selected as the path target, and its value is set as the path y=[x(t 0 ), x′( t 0 ), x(t 1 ), x′(t 1 ), …, x(t f ), x′(t f )] multiple different UAV path target values in T , that is, the part in y parameters are known.
进一步,所述的步骤S5具体为:Further, the step S5 is specifically:
S501:根据需求设定含参数待定控制函数u(t)的类型;通常为减少计算量和保证精度,选取1次或者2次关于时间t含参数多项式类型。S501: Set the type of the undetermined control function u(t) with parameters according to requirements; usually, to reduce the amount of calculation and ensure the accuracy, one or two polynomial types with parameters about time t are selected.
S502:利用深度学习对Fy=B进行求解,训练控制函数u(t)的参数;具体为:(1)将在区间范围内随机选点作为控制函数的参数代入B得到求解得到(2)判断对应元素距离步骤S4中设定的多个路径目标的数值y是否都符合设定的误差范围要求,如果符合则停止训练,输出控制函数,如果不符合,则继续训练直到符合为止。所述的深度学习技术通常采用神经网络。S502: Use deep learning to solve Fy=B, and train the parameters of the control function u(t); specifically: (1) Substitute randomly selected points within the interval as parameters of the control function into B to obtain solve get (2) Judgment Whether the numerical values y of the multiple path targets set in the corresponding element distance step S4 all meet the set error range requirements, if so, stop training and output the control function, if not, continue training until it meets. The deep learning techniques described generally employ neural networks.
可优选的,本发明的步骤S1-S3可以采用离线计算的方式获取无人机的对应线性关系矩阵,然后,再采用在线的方式,在应用中直接导入动态环境几何方法求解的路径目标点,直接求解出最优的路径控制参数。Preferably, in steps S1-S3 of the present invention, the corresponding linear relationship matrix of the UAV can be obtained by offline calculation, and then, the path target point solved by the dynamic environment geometry method can be directly imported in the application in an online mode, The optimal path control parameters are directly solved.
可优选的,本发明方法的经过步骤S1-S3得出线性关系矩阵后,可直接用来计算已知控制函数的高精度路径,即已知F和B,求解y。Preferably, after the linear relationship matrix is obtained through steps S1-S3 of the method of the present invention, it can be directly used to calculate the high-precision path of the known control function, that is, given F and B, to solve y.
特别需要说明地,为了计算精度的提高可以增加路径目标的个数,或细分步长hi,但同时会增加计算量,然而同样的步长划分时间区间的情况下,本发明方法的误差是最小的。In particular, in order to improve the calculation accuracy, the number of path targets can be increased, or the step size h i can be subdivided, but at the same time, the amount of calculation will be increased. However, when the same step size divides the time interval, the error of the method of the present invention is the smallest.
本发明的有益效果在于:本发明提供了一种高精度无人机实时路径规划方法,将无人机误差最小的多目标路径规划问题转化为线性方程求解问题,大幅度降低计算量,通过实时导入动态几何方法求解的多个目标,并采用深度学习方法求出高精度的控制函数,实现了无人机实时高精度路径规划。The beneficial effects of the present invention are as follows: the present invention provides a high-precision real-time path planning method for unmanned aerial vehicles, which converts the multi-objective path planning problem with the smallest unmanned aerial vehicle error into a linear equation solving problem, greatly reduces the amount of calculation, Importing multiple targets solved by dynamic geometric methods, and using deep learning methods to obtain high-precision control functions, realizes real-time high-precision path planning for UAVs.
附图说明Description of drawings
为了使本发明的目的、技术方案,本发明提供如下附图进行说明:In order to make the purpose and technical solution of the present invention, the present invention provides the following drawings for description:
图1为一种高精度无人机实时路径规划方法流程图;Fig. 1 is a flow chart of a real-time path planning method for a high-precision UAV;
图2为本发明实施例1中F和B的叠加示意图。FIG. 2 is a schematic diagram of superposition of F and B in Example 1 of the present invention.
具体实施方式Detailed ways
为使本发明的目的和技术方案更加清晰明白,下面结合附图及实施例对本发明进行详细的描述。In order to make the objectives and technical solutions of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and embodiments.
实施例1:以荣辉等发表论文“基于Matlab无人机数学模型仿真分析与研究”所述的一个无人机为例,现需要控制该无人机执行t∈[0,10]秒内的纵向飞行,初始时,所有状态变量都为0,在特定时间t=5秒时,要求无人机环境约束条件为飞行速度为V(5)=1m/s2,俯仰角为θ(5)=0°,高度H(5)=10m;自身约束条件为升降舵偏角不大于30°。本发明提供“一种高精度无人机实时路径规划方法”,结合图1,该方法包含以下步骤:Example 1: Take a UAV described in the paper "Simulation Analysis and Research of UAV Mathematical Model Based on Matlab" published by Rong Hui et al. as an example, it is now necessary to control the UAV to execute within t∈[0,10] seconds At the beginning, all state variables are 0. At a specific time t=5 seconds, the environmental constraints of the UAV are required to be the flight speed of V(5)=1m/s 2 and the pitch angle of θ(5 )=0°, height H(5)=10m; the self-constraint condition is that the elevator deflection angle is not greater than 30°. The present invention provides "a high-precision real-time path planning method for unmanned aerial vehicles". In conjunction with Fig. 1, the method includes the following steps:
S1:采用基于小扰动线性化原理,建立无人机运动学线性状态微分方程,获取无人机初始时刻状态数据。S1: Using the principle of linearization based on small disturbance, establish a linear state differential equation of UAV kinematics, and obtain state data at the initial moment of UAV.
按照论文所述的纵向运动常系数微分方程形如:x′(t)=dx(t)/dt=A·x(t)+u(t),According to the constant coefficient differential equation of longitudinal motion described in the paper, the form is as follows: x'(t)=dx(t)/dt=A·x(t)+u(t),
其中,A=Along为5×5维常系数矩阵,根据无人机的动力学模型来确定;u(t)=Blong·[δe,δT]T为关于时间的控制向量;x(t)=[V,α,q,θ,H]T为无人机的状态向量,其中,V为飞行速度,α为迎角,q为俯仰角速度,θ为俯仰角,H为高度。Among them, A=A long is a 5×5-dimensional constant coefficient matrix, which is determined according to the dynamic model of the UAV; u(t)=B long ·[δ e , δ T ] T is the control vector about time; x (t)=[V, α, q, θ, H] T is the state vector of the UAV, where V is the flight speed, α is the angle of attack, q is the pitch angular velocity, θ is the pitch angle, and H is the height.
S2:利用插值方法建立无人机运动学误差模型。具体为:S2: Use the interpolation method to establish the kinematic error model of the UAV. Specifically:
首先,将时间区间按1秒为单位等分为10份,对于任意一份hi=1;First, divide the time interval into 10 equal parts in units of 1 second, for any part h i =1;
然后,采用插值方法为Hermit三次样条插值法,在任意时间区间t∈[ti,ti+1]上通过插值方法能够得到一个步骤1所述的常系数微分方程在任意维度上一个的近似解的符号表达式其中,τ=t-ti,α0(τ)=2τ3-3τ2+1,α1(τ)=τ3-τ2+τ,β0(τ)=-2τ3+3τ2,β1(τ)=τ3-τ2,τ∈[0,1];Then, using the interpolation method of Hermit cubic spline interpolation method, in any time interval t∈[t i , t i+1 ], a constant coefficient differential equation described in step 1 can be obtained by interpolation method in any dimension. symbolic expressions for approximate solutions Wherein, τ=tt i , α 0 (τ)=2τ 3 -3τ 2 +1, α 1 (τ)=τ 3 -τ 2 +τ, β 0 (τ)=-2τ 3 +3τ 2 , β 1 (τ)=τ 3 -τ 2 , τ∈[0, 1];
最后,构建无人机运动学在t∈[ti,ti+1]段的误差为进一步,对误差进行二次型建模得到无人机运动学误差模型 Finally, the error of constructing the UAV kinematics in the segment t∈[t i , t i+1 ] is Further, quadratic modeling of the error is carried out to obtain the kinematic error model of the UAV
S3:将无人机运动学误差模型的求解问题等效转化为线性方程求解问题。具体为:S3: The problem of solving the UAV kinematic error model is equivalently transformed into the problem of solving linear equations. Specifically:
S301:针对每一个分段代入Hermit三次样条插值方程,进一步,得到分段误差二次型其中,yi=[x(ti),x′(ti),x(ti+1),x′(ti+1)]T,ai=α′0E-α0A,bi=α′1E-α1A,ci=β′0E-β0A,di=β′1E-β1A,E为5×5维的单位矩阵;S301: For each segment Substitute into the Hermit cubic spline interpolation equation, and further, obtain the quadratic form of piecewise error where y i =[x(t i ), x'(t i ), x(t i+1 ), x'(t i+1 )] T , a i =α′ 0 E-α 0 A, b i =α′ 1 E-α 1 A, c i =β′ 0 E-β 0 A, d i =β′ 1 E-β 1 A, E are 5×5 dimensional identity matrix;
S302:由于恒大于0,且当控制变量确定时为一个常数,因此从误差二次型中删除该项;S302: Due to is always greater than 0, and is a constant when the control variable is determined, so this item is deleted from the error quadratic;
S303:无人机运动学误差模型转化为obj=min(yTFy-2By),其中,F为110×110维由Fi的对应叠加矩阵,B为110维由Bi对应叠加的向量,y=[x(t0),x′(t0),x(t1),x′(t1),…,x(tf),x′(tf)]T为110维yi的叠加;S303: Convert the UAV kinematic error model to obj=min(y T Fy-2By), where F is a 110×110-dimensional corresponding superposition matrix of F i , and B is a 110-dimensional vector superimposed by B i correspondingly, y=[x(t 0 ), x'(t 0 ), x(t 1 ), x'(t 1 ), ..., x(t f ), x'(t f )] T is 110 dimensions y i superposition;
S304:F为满秩且为带状矩阵,obj=min(yTFy-2By)存在唯一解,且解为Fy=B。S304: F is full rank and is a band matrix, there is a unique solution to obj=min(y T Fy-2By), and the solution is Fy=B.
S4:根据环境约束条件设定多个不同的无人机路径目标。具体为:S4: Set multiple different UAV path targets according to environmental constraints. Specifically:
根据环境约束条件,将初始条件x(0)和x′(0)的全部数据以及x(5)中的部分已知环境数据导入y=[x(0),x′(0),x(1),x′(1),…,x(10),x′(10)]T。According to the environmental constraints, import all the data of the initial conditions x(0) and x'(0) and some known environmental data in x(5) into y=[x(0), x'(0), x( 1), x'(1), ..., x(10), x'(10)] T .
S5:将无人机路径目标作为边界条件带入无人机的线性方程,并设定控制函数类型,利用深度学习方法进行矩阵分解求解控制函数参数。具体为:S5: Bring the UAV path target as a boundary condition into the UAV's linear equation, set the control function type, and use the deep learning method to perform matrix decomposition to solve the control function parameters. Specifically:
S501:根据需求设定含参数待定控制函数u(t)为10段的分段函数,其中,第i段为u(t)=Blong·[ait2+bit+ci,dit2+eit+fi]T,ai、bi、ci、di、ei、fi为待定参数。S501: According to the requirement, set the undetermined control function u(t) with parameters as a piecewise function with 10 segments, wherein, the ith segment is u(t)=B long ·[a i t 2 +b i t+c i , d i t 2 +e i t+fi ] T , a i , bi , c i , d i , e i , and f i are parameters to be determined.
S502:利用神经网络对Fy=B进行求解,训练控制函数u(t)的参数;具体为:(1)将在区间范围内随机选点作为控制函数的参数代入B得到求解得到(2)判断对应元素距离初始条件x(0)和x′(0)的全部数据以及x(5)中的部分已知环境数据是否都符合设定的误差范围要求,如果符合则停止训练,输出控制函数,如果不符合,则继续训练直到符合为止。S502: Use a neural network to solve Fy=B, and train the parameters of the control function u(t); specifically: (1) Substitute randomly selected points within the interval as parameters of the control function into B to obtain solve get (2) Judgment Whether all the data of the corresponding element distance from the initial conditions x(0) and x'(0) and some known environmental data in x(5) meet the set error range requirements, if so, stop training, output the control function, If not, continue training until it does.
S6:判断求解的控制参数计算出的升降舵偏角是否满足无人机自身约束条件为升降舵偏角不大于30°,如果满足,则执行控制飞行;如果不满足,则删除该类解,并重新执行步骤S5进行计算,直到满足无人机自身约束条件后执行飞行,或达到预设计算时间上限输出无解预警。S6: Judging whether the elevator declination angle calculated by the solved control parameters satisfies the UAV's own constraint condition that the elevator declination angle is not greater than 30°, if so, execute the control flight; if not, delete this type of solution and restart Execute step S5 to perform calculation until the UAV's own constraints are met, and then the flight is executed, or the preset calculation time upper limit is reached, and a no-solution warning is output.
实施例2:同样,以荣辉等发表论文“基于Matlab无人机数学模型仿真分析与研究”所述的一个无人机为例,现需要控制该无人机执行t∈[0,10]秒内的纵向飞行,初始时,所有状态变量都为0,现计划采用u(t)=Blong·[t2+2t,2t]T作为控制向量,需预测无人机在飞行过程的所有状态。本发明提供“一种高精度无人机实时路径规划方法”,包含以下步骤:Example 2: Similarly, taking a UAV described in the paper "Analysis and Research on Mathematical Model of UAV Based on Matlab" published by Rong Hui as an example, it is now necessary to control the UAV to execute t∈[0,10] Longitudinal flight within seconds, initially, all state variables are 0, and it is planned to use u(t)=B long ·[t 2 +2t, 2t] T as the control vector, it is necessary to predict all the UAV in the flight process. state. The present invention provides "a high-precision UAV real-time path planning method", which includes the following steps:
S1:建立无人机运动学线性状态微分方程,获取无人机自身约束条件和环境约束条件;S1: Establish the linear differential equation of UAV kinematics, and obtain the UAV's own constraints and environmental constraints;
S2:利用插值方法建立无人机运动学误差模型;S2: Use the interpolation method to establish the kinematic error model of the UAV;
S3:将无人机运动学误差模型的求解问题等效转化为线性方程求解问题;S3: Convert the solving problem of the UAV kinematic error model into a linear equation solving problem equivalently;
S4:将初始条件和控制向量代入线性方程进行求解,得到无人机在飞行过程的所有状态。S4: Substitute the initial conditions and control vector into the linear equation to solve, and obtain all the states of the UAV during the flight process.
最后说明的是,以上优选实施例仅用以说明本发明的技术方案而非限制,尽管通过上述优选实施例已经对本发明进行了详细的描述,但本领域技术人员应当理解,可以在形式上和细节上对其做出各种各样的改变,而不偏离本发明权利要求书所限定的范围。Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should Various changes may be made in details without departing from the scope of the invention as defined by the claims.
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